Abstract
Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.
| Original language | English (US) |
|---|---|
| Title of host publication | 2004 IEEE PES Power Systems Conference and Exposition |
| Pages | 1159-1163 |
| Number of pages | 5 |
| Volume | 2 |
| State | Published - 2004 |
| Event | 2004 IEEE PES Power Systems Conference and Exposition - New York, NY, United States Duration: Oct 10 2004 → Oct 13 2004 |
Other
| Other | 2004 IEEE PES Power Systems Conference and Exposition |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 10/10/04 → 10/13/04 |
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Keywords
- Artificial neural networks
- Composite load modeling
- Power systems
- Stability analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Composite neural network load models for power system stability analysis. / Keyhani, Ali; Lu, Wenzhe; Heydt, Gerald T.
2004 IEEE PES Power Systems Conference and Exposition. Vol. 2 2004. p. 1159-1163.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Composite neural network load models for power system stability analysis
AU - Keyhani, Ali
AU - Lu, Wenzhe
AU - Heydt, Gerald T.
PY - 2004
Y1 - 2004
N2 - Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.
AB - Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.
KW - Artificial neural networks
KW - Composite load modeling
KW - Power systems
KW - Stability analysis
UR - http://www.scopus.com/inward/record.url?scp=15944390922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=15944390922&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:15944390922
SN - 078038718X
SN - 9780780387188
VL - 2
SP - 1159
EP - 1163
BT - 2004 IEEE PES Power Systems Conference and Exposition
ER -